Definitions - Life Sciences Lens

21 CFR Part 11: Regulations by the US Food and Drug Administration (FDA) that specify requirements for GxP systems.

ALCOA+: Framework to verify data integrity based on the following principles:

Attributable
Legible
Contemporaneously recorded, original, or a true copy
Accurate
Complete
Consistent
Enduring
Available
Traceable

Bioinformatics: The application of computational tools and methods to analyze, interpret, and manage biological data, including genomic sequences, protein structures, and molecular interactions.

Biomarker: Measurable biological indicator (such as a gene, protein, or metabolite) that can be objectively measured to assess normal biological processes, disease states, or responses to therapeutic interventions.

Clinical Data Interchange Standards Consortium (CDISC): Organization that develops data standards for the pharmaceutical industry. CDISC standards are used to provide study data for FDA submissions.

Change control: A systematic process for managing modifications to validated systems, verifying that changes are documented, tested, approved, and implemented without compromising system integrity or regulatory adherence.

Contract research organization (CRO): Third-party company that provides research and development services (including those in support of clinical trials) to pharmaceutical, biotechnology, and medical device industries.

Data governance: Framework and implementation of policies to verify data quality, security, privacy, and regulatory adherence throughout the data lifecycle.

Data lineage: Tracking and maintaining documentation related to the flow of data from its origin through its use, transformation, and storage in a regulated system.

Data sovereignty: The concept that data is subject to the laws and governance structures of the country or region where it is collected or stored, which is particularly important for patient data or other data used in clinical trials.

Digital Imaging and Communications in Medicine (DICOM): A standard for handling, storing, printing, and transmitting medical imaging information and related data. For more detail, see Medical Imaging on AWS.

Digital twin: Virtual representation of a physical system or process that enable experimentation, simulation, analysis, and prediction of outcomes.

Drug discovery: Process of identifying and developing new pharmaceutical compounds for treating specific diseases or conditions.

Electronic Lab Notebook (ELN): Digital system that replaces traditional paper laboratory notebooks, enabling researchers to capture, organize, search, and share experimental data, protocols, and observations. ELNs provide audit trails for regulatory adherence, improve data integrity through version control and electronic signatures, and integrate with laboratory systems such as LIMS.

FAIR data principles: Guidelines for managing scientific data to promote data sharing and collaboration in research environments. FAIR stands for findable, accessible, interoperable, and reusable.

Fast Healthcare Interoperability Resources (FHIR): Data standard for storing and exchanging healthcare information electronically and enabling interoperability between healthcare systems.

Genomics or omics: The study and use of genetic sequencing and related data to produce actionable insights which also includes proteomics and transcriptomics.

General Data Protection Regulation (GDPR): European Union regulation governing data protection and privacy. For more detail, see General Data Protection (GDPR) Center.

GxP: Good practice guidelines that improve quality and cover a variety of life sciences workloads, including (but not limited to) Good Laboratory Practices (GLP), Good Clinical Practices (GCP), and Good Manufacturing Practices (GMP).

ISA-95: International standard that defines a framework for integrating enterprise business systems with manufacturing control systems. It establishes communication interfaces between operational layers in industrial environments.

Pharmacovigilance: The science and activities related to detecting, assessing, understanding, and blocking adverse effects or other drug-related problems throughout the lifecycle of pharmaceutical products. Includes systematic monitoring and evaluation of safety information from healthcare providers and patients, with regulatory requirements for adverse event reporting and risk management.

Quality management system (QMS): System that documents processes, procedures, and responsibilities for achieving quality policies and objectives to meet regulatory requirements in life sciences organizations. For more detail, see resources in AWS Artifact.

Health Insurance Portability and Accountability Act (HIPAA): United States federal law that establishes privacy and security standards for protecting patient data stored or transmitted by regulated entities.

Laboratory information management system (LIMS): Software system that manages laboratory operations, including sample tracking, workflow automation, data management, instrument integration, and quality control for analytical and research laboratories. LIMS systems provide chain of custody documentation and verify data integrity and traceability required for GxP adherence.

Molecular dynamics: Computational simulation methods used to analyze the physical movements and interactions of atoms and molecules over time. For more detail, see High Performance Computing.

Picture Archiving and Communication System (PACS): Medical imaging technology or systems that store, retrieve, manage, distribute, or make available medical images.

Protected health information (PHI): Individually identifiable health information that is stored or transmitted and subject to regulatory requirements, such as HIPAA.

Real world data (RWD): Data collected from real-world sources outside of clinical trials including electronic health records, claims databases, and patient registries. This provides a broader set of data in contrast to that collected in clinical trials which is limited to specific trial cohorts and protocols.

Real world evidence (RWE): Clinical evidence derived from analysis of real-world data (RWD), used to support regulatory decisions and demonstrate the efficacy of treatments or therapies in real-world settings.

Semantic layer: Abstraction layer providing business-friendly definitions and relationships for underlying data, enabling consistent interpretation across different systems and users.

Quality Risk Management (QRM): Systematic approach to identifying, assessing, controlling, communicating, and reviewing risks to pharmaceutical product quality throughout the product lifecycle, supporting science-based decision-making with the primary goal of patient safety.

Validation (like frameworks, approaches, and protocols): Systematic processes to verify that systems, processes, and methods consistently produce results that meet predetermined specifications and quality attributes.